code: def test_pytorch_openvino_model_additional_attrs(self):
      model = mobilenet_v3_small(num_classes=10)
      # patch a attribute
      model.channels = 3
  
      def hello():
          print("hello world!")
  
      # patch a function
      model.hello = hello
  
      x = torch.rand((10, 3, 256, 256))
      y = torch.ones((10,), dtype=torch.long)
  
      ds = TensorDataset(x, y)
      dataloader = DataLoader(ds, batch_size=2)
  
      openvino_model = InferenceOptimizer.quantize(
          model,
          accelerator="openvino",
          calib_data=dataloader,
          metric=F1Score("multiclass", num_classes=10),
          thread_num=2,
      )
      assert openvino_model.channels == 3
      openvino_model.hello()
      with pytest.raises(
          AttributeError,
          match="'PytorchOpenVINOModel' object has no attribute 'width'",
      ):
          openvino_model.width
  
      with InferenceOptimizer.get_context(openvino_model):
          assert torch.get_num_threads() == 2
          y1 = openvino_model(x[0:1])
  
      # save & load without original model
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          InferenceOptimizer.save(openvino_model, tmp_dir_name)
          load_model = InferenceOptimizer.load(
              tmp_dir_name, device="CPU"
          )
      with pytest.raises(
          AttributeError,
          match="'PytorchOpenVINOModel' object has no attribute 'channels'",
      ):
          load_model.channels
      with pytest.raises(AttributeError):
          load_model.hello()
  
      # save & load with original model
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          InferenceOptimizer.save(openvino_model, tmp_dir_name)
          load_model = InferenceOptimizer.load(
              tmp_dir_name, model=model, device="CPU"
          )
      assert load_model.channels == 3
      load_model.hello()
      with pytest.raises(
          AttributeError,
          match="'PytorchOpenVINOModel' object has no attribute 'width'",
      ):
          openvino_model.width
  
imports: from unittest import TestCase
  from torchmetrics import F1Score
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch import InferenceOptimizer
  from torchvision.models.mobilenetv3 import mobilenet_v3_small
  from torchvision.models import resnet50
  import torch
  from torch import nn
  from torch.utils.data.dataset import TensorDataset
  from torch.utils.data.dataloader import DataLoader
  import tempfile
  import pytest
  import numpy
label_desc: Use image node_id 6 for calling the TestOpenVINO obj's underlying member method code with example usage: obj.test_pytorch_openvino_model_additional_attrs() without return types